CALIBRATING pMRI WITH CARTESIAN CONTINUOUS SAMPLING

ABSTRACT

Example systems, methods, and apparatus control a pMRI apparatus to produce a pulse sequence having an extended acquisition window, and overlapping phase-encoding gradients and read gradients. One example method controls a pMRI apparatus to produce a trajectory having Cartesian and radial segments that sample in a manner that satisfies the Nyquist criterion in at least one region of a volume to be imaged. The pMRI apparatus is controlled to apply radio frequency energy to the volume according to the pulse sequence and following the trajectory and to acquire MR signal from the volume in response to the application of the RF energy. The MR signal includes a first component associated with the Cartesian segment of the trajectory and a second component associated with the radial segment of the trajectory. The example method includes calibrating a reconstruction process using Nyquist-satisfying data from the second component.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Patent60/927,224, filed May 2^(nd), 2007, by the same inventors.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction of the patent document or thepatent disclosure as it appears in the Patent and Trademark Officepatent file or records, but otherwise reserves all copyright rightswhatsoever.

BACKGROUND

Parallel Magnetic Resonance Imaging (pMRI) apparatus acquire signals inparallel using an array of detectors (e.g., coils). The detectors may bearranged in a phased array of coils. Individual coils in the phasedarray are generally designed to have localized sensitivity, or thesensitivities of coils may be designed to be smooth over a field of view(FOV) and may overlap. Reconstruction in pMRI depends on understandingthe actual sensitivity of these coils in a pMRI apparatus during an MRIsession. For example, some arrays may have 2, 4, 8, 16, 32, or morecoils, some of which may have slight individualities, both asmanufactured and as deployed. Additional individualities may appearduring an imaging session based on the unknown position of the detectors(e.g. in a flexible array) or due to actual dynamic conditions duringimage acquisition (e.g., motion, noise, variable coil loading). Thus,actual results may differ from those predicted theoretically.Understanding the actual sensitivity of coils may depend, at least inpart, on a per-session calibration of the coils.

pMRI techniques that under sample k-space have been developed. Thesetechniques typically acquire additional coil sensitivity information tooffset the effect of the under sampling. The additional coil sensitivityinformation has conventionally been computed from additional k-spacelines acquired specifically for calibration. These lines may be referredto as auto-calibration signal (ACS) lines. Conventionally, a smallnumber of ACS lines are acquired before and/or during a scan to helpestimate sensitivities. Thus, at least part of the benefit of undersampling is lost due to the additional time required to acquire ACSlines. Under sampling may lead to a reduction in scan time that isreferred to as a reduction factor (R). In some conventional systems,(R-1) extra ACS lines are acquired from near the center of k-space atpositions like mΔk_(y), where m counts from 1 to (R−1). With thecalibration data available, a reconstruction that includes calculatingmissing k-space data is based, at least in part, on coil sensitivitiescomputed from the ACS lines may be undertaken.

GRAPPA (Generalized Autocalibrating Partially Parallel Acquisitions) isone technique that reconstructs in k-space by calculating missingk-space data based, at least in part, on information acquired from ACSlines. In GRAPPA, the additionally acquired ACS lines S_(k) ^(ACS) areused to automatically derive a set of linear weights n_(k) ^((m)). InGRAPPA, missing k-space data can be calculated from measured k-spacedata in light of the sensitivities and weights to form a complete densek-space, resulting in a full field of view (FOV) after Fouriertransformation.

In GRAPPA, the component coil signals S_(k)(k) are fit to a singlecomponent coil ACS signal. This procedure is repeated for the componentcoils. Thus, it can be seen that GRAPPA uses multiple k-space lines frommultiple coils to fit one single coil ACS line. This results in improvedaccuracy in the fit procedure over, for example, a system determinedfrom external reference data, which could include static maps of coilsensitivity. Central k-space lines may be fit to calculatereconstruction parameters. Since this fitting procedure involves globalinformation, it may be less affected by local inhomogeneities.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate various example systems, methods,and other example embodiments of various aspects of the invention. Itwill be appreciated that the illustrated element boundaries (e.g.,boxes, groups of boxes, or other shapes) in the figures represent oneexample of the boundaries. One of ordinary skill in the art willappreciate that in some examples one element may be designed as multipleelements or that multiple elements may be designed as one element. Insome examples, an element shown as an internal component of anotherelement may be implemented as an external component and vice versa.Furthermore, elements may not be drawn to scale.

FIG. 1 illustrates a portion of a pulse sequence having an extendedacquisition window, and overlapping phase-encoding gradients and readgradients.

FIG. 2 illustrates a portion of a hybrid k-space trajectory thatincludes both Cartesian and radial segments.

FIG. 3 illustrates a portion of an acquisition resulting from a hybridk-space trajectory and a pulse sequence having an extended acquisitionwindow, and overlapping phase-encoding gradients and read gradients.

FIG. 4 illustrates sampling regions associated with data associated witha parallel reconstruction scheme performed on data acquired using ahybrid k-space trajectory and a pulse sequence having an extendedacquisition window, and overlapping phase-encoding gradients and readgradients.

FIG. 5 illustrates a method for performing parallel acquisitionreconstruction that includes calibration based on data acquired inresponse to a k-space trajectory having both Cartesian and radialsegments and a pulse sequence having an extended acquisition window, andoverlapping phase-encoding and read gradients.

FIG. 6 illustrates a method for performing parallel acquisitionreconstruction that includes calibration based on data acquired inresponse to a k-space trajectory having both Cartesian and radialsegments and a pulse sequence having an extended acquisition window, andoverlapping phase-encoding and read gradients.

FIG. 7 illustrates a pMRI apparatus configured to perform parallel,acquisition reconstruction that includes calibration based ondata-acquired in response to a k-space trajectory having both Cartesianand radial segments and a pulse sequence having an extended acquisitionwindow, and overlapping phase-encoding and read gradients.

FIG. 8 illustrates a pMRI apparatus configured to perform parallelacquisition reconstruction that includes calibration based on dataacquired in response to a k-space trajectory having both Cartesian andradial segments and a pulse sequence having an extended acquisitionwindow, and overlapping phase-encoding and read gradients.

FIG. 9 illustrates a computing device in which example methods describedherein may be performed to control image reconstruction that includescalibration based on data acquired in response to a k-space trajectoryhaving both Cartesian and radial segments and a pulse sequence having anextended acquisition window, and overlapping phase-encoding and readgradients.

DETAILED DESCRIPTION

Example systems and methods facilitate calibrating a parallelreconstruction process using Cartesian continuous sampling withoutacquiring ACS lines. In one example, systems and methods may calibrate aGRAPPA reconstruction using both Cartesian continuous sampling and ACSlines. The number of ACS lines may be smaller than in conventional ACSline calibration systems. Recall that the Fourier-domain GRAPPAtechnique reduces scan time by sampling at a rate less than the Nyquistcriterion. In Cartesian pMRI, the under sampling involves skippingphase-encoding steps. Since phase-encoding steps are skipped, less thanall the k-space data is acquired. Thus, the “missing” k-space data mayneed to be computed from available data. While GRAPPA techniques aredescribed, it is to be appreciated that other parallel acquisitiontechniques may be employed.

Consider an image with a desired resolution of N_(y) lines in thephase-encoding direction. To achieve a reduction (a.k.a. acceleration)factor (R) of 2, only N_(y)/2 k-space lines may be acquired. Missingk-space data points in a single channel of a detector array can bereconstructed using a linear combination of acquired data points fromall channels. However, for an accurate reconstruction, in vivo coilsensitivity calibrations are required. These in vivo coil sensitivitycalibrations have typically relied on ACS lines. Conventionally, anadditional number (N_(acs)) of ACS lines that satisfy the Nyquistcriterion are acquired. Thus, (N_(y)/R+N_(acs)) k-space lines areconventionally acquired, where N_(acs) is typically in the range of 16to 32, with the actual number depending on the number of channels andthe desired reduction factor.

Example systems and methods do not require the N_(acs) additionalk-space lines, instead relying on data acquired using a Cartesiancontinuous sampling sequence having an extended acquisition window. TheCartesian continuous sampling sequence acquires data during imaginggradients. Thus, rather than acquiring additional ACS lines, examplesystems and methods may continuously sample in k-space. While“continuous” sampling is described, one skilled in the art willappreciate that “substantially continuous” sampling may occur.Additionally, periodic sampling during gradient changing may be employedand may be considered to be “continuous sampling” as used herein.Conventional systems do not sample as gradients are changed to movebetween k-space lines. For example, conventional systems do not sampleas gradients are changed to move to outer k-space lines. The examplesystems and methods here may continue to sample during these periods.This sampling uses rather than wastes the time spent changing thegradient to move to the outer k-space lines thereby amortizing the costof moving between k-space lines. Additionally, example systems andmethods may acquire one or more ACS lines to facilitate blendedcalibration.

Changing a gradient is not instantaneous. Additionally, the speed atwhich a trajectory moves is related to the gradient strength. So, ratherthan simply sit idle and not sample while a gradient is being changed,example systems and methods may continue to sample during the motion. Inthis way, a system that is going to spend the time to do a traversal toan outer k-space line will sample and acquire data that can be employedfor calibration. Thus, the continuous sampling removes the need toacquire additional ACS lines, or reduces the number of required ACSlines, which in turn reduces the overall time required for an imagingsession. While the need to acquire ACS lines is removed or reduced, someexample systems and methods may still acquire an ACS line(s).

FIG. 1 illustrates a portion 100 of a pulse sequence having an extendedacquisition window 110, and an overlapping phase-encoding gradient 130and read gradient 140. The overlapping phase-encoding gradients 130 andread gradients 140 yield a trajectory having both Cartesian and radialsegments. FIG. 2 illustrates a trajectory 200 having both Cartesiansegments (e.g., segment 210, segment 220) and radial segments (e.g.,segment 230, segment 240). Returning now to FIG. 1, note the extendedacquisition window 110 as compared to a conventional acquisition window120. Extending the acquisition window facilitates acquiring additionaldata that can be used for calibration. The radial portion of theresulting trajectory coupled with additional sampling causes centralk-space to be over-sampled with respect to the desired under sampling.While the extended acquisition window 110 is illustrated encompassingall of the portion 100 of the pulse sequence, it is to be appreciatedthat the extended acquisition window may cover less than all of portion100.

In one example, the radial portion of the resulting trajectory leads tocentral k-space being sampled in a manner that satisfies the Nyquistcriterion. Thus, this critically and over-sampled data may be used tocalibrate a reconstruction process. In one example the reconstructionprocess may be a GRAPPA reconstruction process. After the calibration isperformed, data in an under-sampled Cartesian area can be reconstructedbased, at least in part, on the data acquired in the region thatsatisfies the Nyquist criterion and the calibration data.

FIG. 2 illustrates a k-space trajectory 200. The trajectory 200 includestwo Cartesian segments (e.g., segment 210, segment 220) and two radialsegments (e.g., segment 230, segment 240). Conventionally, samplingwould only occur on lines 210 and 220. Example systems and methods alsosample along segment 230 and segment 240 of the trajectory. In oneexample, sampling will be continuous and/or substantially continuousalong the length of both segment 230 and segment 240. In anotherexample, sampling may occur for only a portion of segment 230 and aportion of segment 240. In yet another example, sampling may becontinuous, period, and/or substantially continuous along either segment230 or segment 240 or may occur for only a portion of segment 230 orsegment 240. The additional data acquired by sampling along segment 230and/or segment 240 can be used to calibrate a reconstruction. Thisadditional sampling may be referred to as continuous Cartesian sampling,even though the sampling may be less than “continuous”.

FIG. 3 illustrates an acquisition 300 of data from k-space that is builtup from a set of trajectory portions like that illustrated in FIG. 2.The acquisition 300 can be configured to sample at least a portion ofthe central k-space area in a manner that satisfies the Nyquistcriteria. Thus, while outer regions of acquisition 300 may beunder-sampled, inner regions (e.g., central k-space) may not beunder-sampled. Indeed, central k-space may be critically sampled andmay, therefore, include sufficient information for calibrating areconstruction. While FIG. 3 illustrates an acquisition 300 wheresampling occurs along every transition between Cartesian lines, examplesystems and methods may not acquire along every transition.

FIG. 4 illustrates an acquisition 400 of data from k-space that is builtup from a set of trajectory segments like that illustrated in FIG. 2.The acquisition 400 illustrates a trajectory 410 along which data wasacquired and a trajectory 420 along which data was not acquired. A fullysampled acquisition may have included acquiring data along bothtrajectory 410 and trajectory 420. Thus, some data may be missing fromk-space. However, acquisition 400 illustrations a region 430 that issampled in a manner that satisfies the Nyquist criteria. Therefore, datasampled from the region 430 during the radial segment of the acquisitionthat satisfies the Nyquist criteria may be used to calibrate thereconstruction process. This data may have been acquired using thecontinuous sampling described in connection with trajectory 200 (FIG. 2)and pulse sequence 100 (FIG. 1). Missing data points in the Cartesianportion of the acquisition (e.g., points in region 440) can bereconstructed based, at least in part, on the additional data. Note thatno additional ACS lines were acquired.

The following includes definitions of selected terms employed herein.The definitions include various examples and/or forms of components thatfall within the scope of a term and that may be used for implementation.The examples are not intended to be limiting. Both singular and pluralforms of terms may be within the definitions.

References to “one embodiment”, “an embodiment”, “one example”, “anexample”, and so on, indicate that the embodiment(s) or example(s) sodescribed may include a particular feature, structure, characteristic,property, element, or limitation, but that not every embodiment orexample necessarily includes that particular feature, structure,characteristic, property, element or limitation. Furthermore, repeateduse of the phrase “in one embodiment” does not necessarily refer to thesame embodiment, though it may.

ASIC: application specific integrated circuit.

CD: compact disk.

CD-R: CD recordable.

CD-RW: CD rewriteable.

DVD: digital versatile disk and/or digital video disk.

HTTP: hypertext transfer protocol.

LAN: local area network.

PCI: peripheral component interconnect.

PCIE: PCI express.

RAM: random access memory.

DRAM: dynamic RAM.

SRAM: synchronous RAM.

ROM: read only memory.

PROM: programmable ROM.

USB: universal serial bus.

WAN: wide area network.

“Computer component”, as used herein, refers to a computer-relatedentity (e.g., hardware, firmware, software in execution, combinationsthereof). Computer components may include, for example, a processrunning on a processor, a processor, an object, an executable, a threadof execution, and a computer. A computer component(s) may reside withina process and/or thread. A computer component may be localized on onecomputer and/or may be distributed between multiple computers.

“Computer communication”, as used herein, refers to a communicationbetween computing devices (e.g., computer, personal digital assistant,cellular telephone) and can be, for example, a network transfer, a filetransfer, an applet transfer, an email, an HTTP transfer, and so on. Acomputer communication can occur across, for example, a wireless system(e.g., IEEE 802.11), an Ethernet system (e.g., IEEE 802.3), a token ringsystem (e.g., IEEE 802.5), a LAN, a WAN, a point-to-point system, acircuit switching system, a packet switching system, and so on.

“Computer-readable medium”, as used herein, refers to a medium thatstores signals, instructions and/or data. A computer-readable medium maytake forms, including, but not limited to, non-volatile media, andvolatile media. Non-volatile media may include, for example, opticaldisks, magnetic disks, and so on. Volatile media may include, forexample, semiconductor memories, dynamic memory, and so on. Common formsof a computer-readable medium may include, but are not limited to, afloppy disk, a flexible disk, a hard disk, a magnetic tape, othermagnetic medium, an ASIC, a CD, other optical medium, a RAM, a ROM, amemory chip or card, a memory stick, and other media from which acomputer, a processor or other electronic device can read.

“Data store”, as used herein, refers to a physical and/or logical entitythat can store data. A data store may be, for example, a database, atable, a file, a list, a queue, a heap, a memory, a register, and so on.In different examples, a data store may reside in one logical and/orphysical entity and/or may be distributed between two or more logicaland/or physical entities.

“Logic”, as used herein, includes but is not limited to hardware,firmware, software in execution on a machine, and/or combinations ofeach to perform a function(s) or an action(s), and/or to cause afunction or action from another logic, method, and/or system. Logic mayinclude a software controlled microprocessor, a discrete logic (e.g.,ASIC), an analog circuit, a digital circuit, a programmed logic device,a memory device containing instructions, and so on. Logic may includeone or more gates, combinations of gates, or other circuit components.Where multiple logical logics are described, it may be possible toincorporate the multiple logical logics into one physical logic.Similarly, where a single logical logic is described, it may be possibleto distribute that single logical logic between multiple physicallogics.

An “operable connection”, or a connection by which entities are“operably connected”, is one in which signals, physical communications,and/or logical communications may be sent and/or received. An operableconnection may include a physical interface, an electrical interface,and/or a data interface. An operable connection may include differingcombinations of interfaces and/or connections sufficient to allowoperable control. For example, two entities can be operably connected tocommunicate signals to each other directly or through one or moreintermediate entities (e.g., processor, operating system, logic,software). Logical and/or physical communication channels can be used tocreate an operable connection.

“Signal”, as used herein, includes but is not limited to, electricalsignals, optical signals, analog signals, digital signals, data,computer instructions, processor instructions, messages, a bit, a bitstream, or other means that can be received, transmitted and/ordetected.

“Software”, as used herein, includes but is not limited to, one or moreexecutable instruction that cause a computer, processor, or otherelectronic device to perform functions, actions and/or behave in adesired manner. “Software” does not refer to stored instructions beingclaimed as stored instructions per se (e.g., a program listing). Theinstructions may be embodied in various forms including routines,algorithms, modules, methods, threads, and/or programs includingseparate applications or code from dynamically linked libraries.

“User”, as used herein, includes but is not limited to one or morepersons, software, computers or other devices, or combinations of these.

Some portions of the detailed descriptions that follow are presented interms of algorithms and symbolic representations of operations on databits within a memory. These algorithmic descriptions and representationsare used by those skilled in the art to convey the substance of theirwork to others. An algorithm, here and generally, is conceived to be asequence of operations that produce a result. The operations may includephysical manipulations of physical quantities. Usually, though notnecessarily, the physical quantities take the form of electrical ormagnetic signals capable of being stored, transferred, combined,compared, and otherwise manipulated in a logic, and so on. The physicalmanipulations create a concrete, tangible, useful, real-world result.

It has proven convenient at times, principally for reasons of commonusage, to refer to these signals as bits, values, elements, symbols,characters, terms, numbers, and so on. It should be borne in mind,however, that these and similar terms are to be associated with theappropriate physical quantities and are merely convenient labels appliedto these quantities. Unless specifically stated otherwise, it isappreciated that throughout the description, terms including processing,computing, determining, and so on, refer to actions and processes of acomputer system, logic, processor, or similar electronic device thatmanipulates and transforms data represented as physical (electronic)quantities.

Example methods may be better appreciated with reference to flowdiagrams. While for purposes of simplicity of explanation, theillustrated methodologies are shown and described as a series of blocks,it is to be appreciated that the methodologies are not limited by theorder of the blocks, as some blocks can occur in different orders and/orconcurrently with other blocks from that shown and described. Moreover,less than all the illustrated blocks may be required to implement anexample methodology. Blocks may be combined or separated into multiplecomponents. Furthermore, additional and/or alternative methodologies canemploy additional, not illustrated blocks.

FIG. 5 illustrates a method 500 for calibrating a parallelreconstruction without acquiring additional ACS lines. Method 500includes, at 510, controlling a pMRI apparatus to produce a pulsesequence. The pulse sequence is to have an extended acquisition window.The pulse sequence is also to have overlapping phase-encoding gradientsand read gradients. A portion of an example pulse sequence isillustrated in FIG. 1.

Method 500 also includes, at 520, controlling the pMRI apparatus toproduce a trajectory having both Cartesian and radial segments. Oneskilled in the art will appreciate that the pulse sequence produced at510 may yield the trajectory having both Cartesian and radial segments.Thus, in one example, method 500 may combine actions 510 and 520 into asingle action. An example trajectory is illustrated in FIG. 2. Using thepulse sequence and the trajectory, a central k-space region may becritically-sampled while a Cartesian segment may be under sampled. Thismay facilitate reducing acquisition time while still acquiringinformation for calibrating a reconstruction process.

Method 500 also includes, at 530, controlling the pMRI apparatus toapply radio frequency (RF) energy to a volume to be imaged. The RFenergy is to be applied according to the pulse sequence and followingthe hybrid trajectory.

Method 500 also includes, at 540, controlling the pMRI apparatus toacquire MR signal from the volume in response to the application of theRF energy. The acquired MR signal will include a first component that isassociated with the Cartesian segment of the trajectory. The acquired MRsignal will also include a second component associated with the radialsegment of the trajectory. The MR signal will satisfy the Nyquistcriteria in at least one region of the volume. In one example, the atleast one region is a central region in k-space.

Acquiring the MR signal may include controlling the pMRI apparatus tosample at different times during the radial segment of the trajectory.For example, the pMRI apparatus may be controlled to continuously sampleduring the radial segment of the trajectory, to sample during at least aportion of the radial segment of the trajectory, and to not sampleduring at least a portion of the radial segment of the trajectory.

Method 500 also includes, at 550, calibrating a reconstruction process.The reconstruction process will be calibrated using Nyquist-satisfyingdata from the second component of the MR signal. Recall that theNyquist-satisfying data is acquired from at least one region (e.g.,central k-space region) in the volume to be imaged.

Method 500 also includes, at 560, reconstructing an image from the MRsignal using the calibrated reconstruction process and, at 570,providing the image reconstructed at 560. The image may be provided to adisplay on which the image will be displayed. The image may also beprovided to a memory where the image will be stored.

FIG. 6 illustrates a method 600 for calibrating a parallelreconstruction (e.g., GRAPPA reconstruction) using both data acquiredduring Cartesian continuous sampling and additional ACS lines. Method600 includes several actions similar to those described in connectionwith method 500 (FIG. 5). For example, method 600 includes controlling apMRI apparatus to produce a pulse sequence at 610, controlling the pMRIapparatus to produce a trajectory at 620, controlling the pMRI apparatusto apply RF energy at 630, controlling the pMRI apparatus to acquire MRsignal at 640, and calibrating a reconstruction at 650. However, method600 may include an additional action.

For example, method 600 includes, at 625, controlling the pMRI apparatusto acquire at least one auto-calibration signal (ACS) line. While “atleast one” ACS line is described, it is to be appreciated that indifferent examples method 600 may include controlling the pMRI apparatusto acquire less than a complete set of ACS lines, to acquire a completeset of ACS lines, and so on. Since an ACS line is acquired by method600, the calibrating at 650 may be based on both Nyquist-satisfying datafrom the second component and an ACS line(s).

FIG. 7 illustrates an apparatus 700 associated with Cartesian continuoussampling. In one example, apparatus 700 may be incorporated into a pMRIapparatus. In another example, apparatus 700 may be external to butoperably connected to a pMRI apparatus. Apparatus 700 includes an array710 of signal receiving coils to receive MR signal from a volume to beimaged. The array 710 may be, for example, a phased array of receivingcoils.

Apparatus 700 also includes a pulse sequence logic 720. The pulsesequence logic 720 controls a pMRI apparatus to produce a pulse sequencehaving an extended acquisition window. The pulse sequence is also tohave overlapping phase-encoding gradients and read gradients. A portionof an example pulse sequence is illustrated in FIG. 1.

Apparatus 700 also includes a trajectory logic 730. The trajectory logic730 is to control the pMRI apparatus to produce a trajectory having bothCartesian and radial segments. While a trajectory logic 730 isillustrated, it is to be appreciated that the trajectory may be producedas a result of the pulse sequence produced by the pulse sequence logic720. Thus, in one example, apparatus may not include a separatetrajectory logic 730. An example trajectory is illustrated in FIG. 2.Conventionally, MR signal would be acquired during the Cartesian portionof the trajectory and would not be acquired during the radial portion ofthe trajectory while a gradient is being changed. Example systems andmethods acquire signal during at least a portion of the radial segmentto acquire information from which a reconstruction process can becalibrated.

Apparatus 700 also includes a RF control logic 740. The RF control logic740 is to control the pMRI apparatus to apply RF energy to a volume tobe imaged. The RF energy is to be applied to the volume according to thepulse sequence and following the trajectory. The RF control logic 740may also control the pMRI apparatus to acquire MR signal from the volumein response to the application of the RF energy. The acquired MR signalwill include a first component associated with the Cartesian segment ofthe trajectory. The MR signal will also include a second component thatis associated with the radial segment of the trajectory. The pulsesequence and trajectory are to produce an MR signal that satisfies theNyquist criteria in at least one region of the value. The at least oneregion may be, for example, a central k-space region. Regions outsidethe at least one region may be under sampled while the at least oneregion may be critically sampled.

In different examples, the RF control logic 740 may control the pMRIapparatus to acquire MR signals during different portions of the radialsegment of the trajectory. In one example, the RF control logic 740controls the pMRI apparatus to continuously sample during the radialsegment of the trajectory while in another example, the RF control logic740 controls the pMRI apparatus to continuously sample during at least aportion of the radial segment of the trajectory. In one example, the RFcontrol logic 740 may even control the pMRI apparatus to not sampleduring at least a portion of the radial segment of the trajectory.

In one example, the RF control logic 740 controls the pMRI apparatus toacquire at least one auto-calibration signal line. In this example, thecalibration logic 750 calibrates the reconstruction process usingNyquist-satisfying data from the second component of the MR signal andthe at least one auto-calibration signal line.

Apparatus 700 also includes a calibration logic 750. The calibrationlogic 750 calibrates a reconstruction process using Nyquist-satisfyingdata from the second component of the MR signal. Note thatNyquist-satisfying data is acquired from the at least one region (e.g.,central k-space region).

In one example, apparatus 700 may also include a reconstruction logic toreconstruct an image from the MR signal. The reconstruction logic willrely on the calibration performed by the calibration logic 750. Theapparatus 700 may also include a display to display the image and/or amemory to store the image.

FIG. 8 illustrates an MRI apparatus 800 configured with a Cartesiancontinuous sampling logic 899. Logic 899 facilitates calibrating areconstruction using data acquired during Cartesian continuous samplingas described above. The Cartesian continuous sampling logic 899 may beconfigured with elements of example apparatus described herein and/ormay perform example methods described herein.

The apparatus 800 includes a basic field magnet(s) 810 and a basic fieldmagnet supply 820. Ideally, the basic field magnets 810 would produce auniform Bo field. However, in practice, the Bo field may not be uniform,and may vary over an object being imaged by the MRI apparatus 800. MRIapparatus 800 may include gradient coils 830 configured to emit gradientmagnetic fields like G_(S), G_(P) and G_(R). The gradient coils 830 maybe controlled, at least in part, by a gradient coils supply 840. In someexamples, the timing, strength, and orientation of the gradient magneticfields may be controlled, and thus selectively adapted during an MRIprocedure.

MRI apparatus 800 may include a set of RF antennas 850 that areconfigured to generate RF pulses and to receive resulting magneticresonance signals from an object to which the RF pulses are directed. Insome examples, how the pulses are generated and how the resulting MRsignals are received may be controlled and thus may be selectivelyadapted during an MRI procedure. Separate RF transmission and receptioncoils can be employed. The RF antennas 850 may be controlled, at leastin part, by a set of RF transmission units 860. An RF transmission unit860 may provide a signal to an RF antenna 850.

The gradient coils supply 840 and the RF transmission units 860 may becontrolled, at least in part, by a control computer 870. In one example,the control computer 870 may be programmed to control a pMRI device asdescribed herein. The magnetic resonance signals received from the RFantennas 850 can be employed to generate an image and thus may besubject to a transformation process like a two dimensional fast FourierTransform (FFT) that generates pixilated image data. The transformationcan be performed by an image computer 880 or other similar processingdevice. The image data may then be shown on a display 890. While FIG. 8illustrates an example MRI apparatus 800 that includes variouscomponents connected in various ways, it is to be appreciated that otherMRI apparatus may include other components connected in other ways.

FIG. 9 illustrates an example computing device in which example methodsdescribed herein, and equivalents, may operate. The example computingdevice may be a computer 900 that includes a processor 902, a memory904, and input/output ports 910 operably connected by a bus 908. In oneexample, the computer 900 may include a Cartesian continuous samplinglogic 930 to facilitate controlling image reconstruction based, at leastin part, on data acquired during gradient transitions. In differentexamples, the logic 930 may be implemented in hardware, software,firmware, and/or combinations thereof. While the logic 930 isillustrated as a hardware component attached to the bus 908, it is to beappreciated that in one example, the logic 930 may be implemented in theprocessor 902.

Thus, logic 930 may provide means (e.g., hardware, software, firmware)for generating a pulse sequence having an extended acquisition window.The pulse sequence also may have overlapping phase-encoding gradientsand read gradients that produce a trajectory having both Cartesiansegments and radial segments. The means may be implemented, for example,as an ASIC programmed to control an image reconstruction computer. Themeans may also be implemented as computer executable instructions thatare presented to computer 900 as data 916 that are temporarily stored inmemory 904 and then executed by processor 902. Logic 930 may alsoprovide means (e.g., hardware, software, firmware) for receiving datafrom a continuous Cartesian sampling produced in response to the pulsesequence. Logic 930 may also provide means (e.g., hardware, software,firmware) for calibrating a reconstruction. The calibration may bebased, at least in part, on calibration data that satisfies the Nyquistcriteria, where the calibration data is acquired from continuousCartesian sampling produced in response to the pulse sequence that hasboth Cartesian and radial segments.

Generally describing an example configuration of the computer 900, theprocessor 902 may be a variety of various processors including dualmicroprocessor and other multi-processor architectures. A memory 904 mayinclude volatile memory and/or non-volatile memory. Non-volatile memorymay include, for example, ROM, PROM, and so on. Volatile memory mayinclude, for example, RAM, SRAM, DRAM, and so on.

A disk 906 may be operably connected to the computer 900 via, forexample, an input/output interface (e.g., card, device) 918 and aninput/output port 910. The disk 906 may be, for example, a magnetic diskdrive, a solid state disk drive, a floppy disk drive, a tape drive, aZip drive, a flash memory card, a memory stick, and so on. Furthermore,the disk 906 may be a CD-ROM drive, a CD-R drive, a CD-RW drive, a DVDROM, and so on. The memory 904 can store a process 914 and/or a data916, for example. The disk 906 and/or the memory 904 can store anoperating system that controls and allocates resources of the computer900.

The bus 908 may be a single internal bus interconnect architectureand/or other bus or mesh architectures. While a single bus isillustrated, it is to be appreciated that the computer 900 maycommunicate with various devices, logics, and peripherals using otherbusses (e.g., PCIE, 1394, USB, Ethernet). The bus 908 can be typesincluding, for example, a memory bus, a memory controller, a peripheralbus, an external bus, a crossbar switch, and/or a local bus.

The computer 900 may interact with input/output devices via the i/ointerfaces 918 and the input/output ports 910. Input/output devices maybe, for example, a keyboard, a microphone, a pointing and selectiondevice, cameras, video cards, displays, the disk 906, the networkdevices 920, and so on. The input/output ports 910 may include, forexample, serial ports, parallel ports, and USB ports. The computer 900can operate in a network environment and thus may be connected to thenetwork devices 920 via the i/o interfaces 918, and/or the i/o ports910. Through the network devices 920, the computer 900 may interact witha network. Through the network, the computer 900 may be logicallyconnected to remote computers. Networks with which the computer 900 mayinteract include, but are not limited to, a LAN, a WAN, and othernetworks.

While example systems, methods, and so on have been illustrated bydescribing examples, and while the examples have been described inconsiderable detail, it is not the intention of the applicants torestrict or in any way limit the scope of the appended claims to suchdetail. It is, of course, not possible to describe every conceivablecombination of components or methodologies for purposes of describingthe systems, methods, and so on described herein. Therefore, theinvention is not limited to the specific details, the representativeapparatus, and illustrative examples shown and described. Thus, thisapplication is intended to embrace alterations, modifications, andvariations that fall within the scope of the appended claims.

To the extent that the term “includes” or “including” is employed in thedetailed description or the claims, it is intended to be inclusive in amanner similar to the term “comprising” as that term is interpreted whenemployed as a transitional word in a claim.

To the extent that the term “or” is employed in the detailed descriptionor claims (e.g., A or B) it is intended to mean “A or B or both”. Whenthe applicants intend to indicate “only A or B but not both” then theterm “only A or B but not both” will be employed. Thus, use of the term“or” herein is the inclusive, and not the exclusive use. See, Bryan A.Gamer, A Dictionary of Modern Legal Usage 624 (2d. Ed. 1995).

To the extent that the phrase “one or more of, A, B, and C” is employedherein, (e.g., a data store configured to store one or more of, A, B,and C) it is intended to convey the set of possibilities A, B, C, AB,AC, BC, and/or ABC (e.g., the data store may store only A, only B, onlyC, A&B, A&C, B&C, and/or A&B&C). It is not intended to require one of A,one of B, and one of C. When the applicants intend to indicate “at leastone of A, at least one of B, and at least one of C”, then the phrasing“at least one of A, at least one of B, and at least one of C” will beemployed.

1. A computer-readable medium storing computer executable instructions that when executed by a computer cause the computer to perform a method, the method comprising: controlling a parallel magnetic resonance imaging (pMRI) apparatus to produce a pulse sequence having an extended acquisition window, where the pulse sequence has overlapping phase-encoding gradients and read gradients, and where the pulse sequence controls the pMRI apparatus to produce a trajectory having both Cartesian and radial segments; controlling the pMRI apparatus to apply radio frequency (RF) energy to a volume to be imaged according to the pulse sequence and following the trajectory; controlling the pMRI apparatus to acquire magnetic resonance (MR) signal from the volume in response to the application of the RF energy, where the MR signal includes a first component associated with the Cartesian segment of the trajectory and where the MR signal includes a second component associated with the radial segment of the trajectory, and where the MR signal satisfies the Nyquist criteria in at least one region of the volume; calibrating a partially parallel acquisition reconstruction process using Nyquist-satisfying data from the second component, where the Nyquist-satisfying data is acquired from the at least one region; reconstructing an image from the MR signal using the calibrated reconstruction process; and providing the image.
 2. The computer-readable medium of claim 1, including controlling the pMRI apparatus to continuously sample during the radial segment of the trajectory.
 3. The computer-readable medium of claim 1, including controlling the pMRI apparatus to sample during at least a portion of the radial segment of the trajectory.
 4. The computer-readable medium of claim 3, including controlling the pMRI apparatus to not sample during at least a portion of the radial segment of the trajectory.
 5. The computer-readable medium of claim 1, where the at least one region is a central k-space region.
 6. The computer-readable medium of claim 5, where the central k-space region is critically-sampled by the trajectory.
 7. The computer-readable medium of claim 1, where the at least a portion of the Cartesian segment is under sampled in the phase encoding direction.
 8. The computer-readable medium of claim 1, including controlling the pMRI apparatus to acquire at least one auto-calibration signal line.
 9. The computer-readable medium of claim 8, including controlling the pMRI apparatus to acquire less than a complete set of auto-calibration signal lines.
 10. The computer-readable medium of claim 9, where calibrating the reconstruction process includes using Nyquist-satisfying data from the second component and at least one auto-calibration signal line from the less than complete set of auto-calibration signal lines.
 11. A parallel MRI (pMRI) apparatus, comprising: an array of signal receiving coils to receive MR signal from a volume to be imaged; a pulse sequence logic to control a pMRI apparatus to produce a pulse sequence having an extended acquisition window, where the pulse sequence has overlapping phase-encoding gradients and read gradients, where the pulse sequence controls the pMRI apparatus to produce a trajectory having both Cartesian and radial segments; a radio-frequency control logic to control the pMRI apparatus to apply radio frequency (RF) energy to the volume to be imaged according to the pulse sequence and following the trajectory and to acquire MR signal from the volume in response to the application of the RF energy, where the MR signal includes a first component associated with the Cartesian segment of the trajectory, and where the MR signal includes a second component associated with the radial segment of the trajectory, and where the MR signal satisfies the Nyquist criteria in at least one region; and a calibration logic to calibrate a reconstruction process using Nyquist-satisfying data from the second component, where the Nyquist-satisfying data is acquired from the at least one region.
 12. The pMRI apparatus of claim 11, including a reconstruction logic to reconstruct an image from the MR signal using the calibrated reconstruction process.
 13. The pMRI apparatus of claim 12, including a display to display the image.
 14. The pMRI apparatus of claim 11, where the RF control logic controls the pMRI apparatus to continuously sample during the radial segment of the trajectory.
 15. The pMRI apparatus of claim 11, where the RF control logic controls the pMRI apparatus to continuously sample during at least a portion of the radial segment of the trajectory.
 16. The pMRI apparatus of claim 11, where the RF control logic controls the pMRI apparatus to not sample during at least a portion of the radial segment of the trajectory.
 17. The pMRI apparatus of claim 11, where the RF control logic controls the pMRI apparatus to critically sample a central k-space region of the volume to be imaged and to under sample a portion of the volume associated with the Cartesian segment of the trajectory.
 18. The pMRI apparatus of claim 11, where the RF control logic controls the pMRI apparatus to acquire at least one auto-calibration signal line and where the calibration logic is to calibrate the reconstruction process using Nyquist-satisfying data from the second component and the at least one auto-calibration signal line.
 19. A computer that calibrates a parallel reconstruction, comprising: a first logic that generates a pulse sequence having an extended acquisition window and having overlapping phase-encoding and read gradients that produce a hybrid trajectory having both Cartesian and radial segments; a second logic that receives data from a continuous Cartesian sampling produced in response to the pulse sequence and the hybrid trajectory; and a third logic that calibrates a parallel reconstruction using data from the continuous Cartesian sampling that satisfies the Nyquist criteria.
 20. A system, comprising: means for generating a pulse sequence having an extended acquisition window, overlapping phase-encoding gradients, and read gradients that produce a trajectory having both Cartesian segments and radial segments; means for receiving data from a continuous Cartesian sampling produced in response to the pulse sequence and the trajectory; and means for calibrating a reconstruction based, at least in part, on calibration data that satisfies the Nyquist criteria, the calibration data being acquired from the continuous Cartesian sampling produced in response to the pulse sequence. 